Practical aspects of PARAFAC modeling of fluorescence excitation‐emission data

By C. Andersen, Rasmus Bro. 2003

602 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/66d09f8e06d9da311802de218c88bdc10d2e45b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Andrzej S. Cichocki
2. Petr Tichavský
3. Tamara G. Kolda
4. Daniel M. Dunlavy
5. Evrim Acar
6. Brett W. Bader
7. C. Cordella
8. Bülent Yener

In Media Technology

1. Andrzej S. Cichocki
2. Tamara G. Kolda
3. Daniel M. Dunlavy
4. Evrim Acar
5. C. Cordella
6. Bülent Yener

In Signal Processing

1. Daniel M. Dunlavy
2. Andrzej S. Cichocki
3. Evrim Acar
4. Tamara G. Kolda
5. Anh Huy Phan
6. Petr Tichavský
7. Brett W. Bader
8. Boris N. Khoromskij

Citations in the corpus

• A scalable optimization approach for fitting canonical tensor decompositions (2011, methodology): “Higher-order tensors are denoted by boldface Euler script letters, e.g., X .”
• Unsupervised Multiway Data Analysis: A Literature Survey (2009, methodology): “Core consistency diagnostic has been commonly applied in the literature [3], [5], [20], [21].”
• A mid level data fusion strategy for the Varietal Classification of Lambrusco PDO wines (2014, positive): “A PARAFAC model, with no pretreatment applied, based on four factors constrained for non-negativity in the second and third mode, was built considering the best compromise between explained variance, core consistency and split half analysis [21] results.”
• Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression (2009, neutral): “[46].”
• PCA: The Basic Building Block of Chemometrics (2012, methodology): “Practical aspects of PARAFAC modelling of fluorescence excitation-emission data Fluorescence 2003 [43]”
• CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping (2012, background): “Introduction CANDECOMP/PARAFAC [1], [2], also known as Canonical polyadic decompos iti n (CPD), is a common tensor factorization which has found applications s uch as in chemometrics [3]–[5], telecommunication [6], [7], analysis of fMRI data [8], time-varyin g EEG spectrum [9], [10], data mining [11”
• Tensor Decompositions and Applications (2009, background): “Andersson and Bro [9] surveyed its use in chemometrics to date.”